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AI-Powered Dynamic Pricing Engine for Logistics Operations

trigent.com

Logistics

Transportation

Technology

Operational Inefficiencies in Manual Pricing

Manual quotation processes relying on emails/calls caused delays, data silos hindered market trend analysis, and lack of predictive pricing capabilities reduced competitive advantage and strategic focus.

About the Client

Third-party logistics provider specializing in truckload, intermodal freight, and LTL services with proprietary TMS software

Strategic Transformation Goals

Automate pricing quotation processes with 95%+ accuracy
Integrate petabyte-scale data from 37+ internal/external sources
Develop ML-driven price prediction model for carrier negotiations
Establish real-time market intelligence integration
Improve quote acceptance rate to 98%+

Core System Capabilities

Real-time price prediction engine with margin optimization
Multi-source data ingestion pipeline (transactional, freight, market data)
Carrier cost estimation with accessorial charge calculation
RESTful APIs for system integration with TMS and ERP platforms
Automated model retraining with new data triggers

Technology Stack

Python (Jupyter Notebook, PyTorch, Scikit-learn)

Azure Machine Learning

MS SQL Database

Redis Cache

Azure Kubernetes Service

System Integrations

Freightwaves market intelligence API
DAT/EIA/McLeod data feeds
Internal TMS platform
Azure Blob Storage data lakes

Operational Requirements

Sub-500ms prediction latency
99.95% system availability
Horizontal scalability for 10M+ transactions/day
GDPR-compliant data handling
Automated model drift detection

Business Transformation Outcomes

Enables 98.3% quote acceptance rates through precise pricing, reduces manual effort by 70%, improves annual load capacity per carrier to 18.59, and establishes data-driven market trend anticipation capabilities.